def stest_one_versus_many(model, data_dir, img_size):
""" """
data_iterator = iter(
DataLoader(
PairDataset(
data_dir,
transform=transforms.Compose([
transforms.Grayscale(),
transforms.Resize(img_size),
transforms.ToTensor(),
]),
split=SplitEnum.testing,
),
num_workers=0,
batch_size=1,
shuffle=True,
))
x0, *_ = next(data_iterator)
for i in range(10):
_, x1, _ = next(data_iterator)
dis = (torch.pairwise_distance(*model(
to_tensor(x0, device=global_torch_device()),
to_tensor(x1, device=global_torch_device()),
)).cpu().item())
boxed_text_overlay_plot(
torchvision.utils.make_grid(torch.cat((x0, x1), 0)),
f"Dissimilarity: {dis:.2f}",
)
def stest_many_versus_many(model, data_dir, img_size, threshold=0.5):
""" """
data_iterator = iter(
DataLoader(
PairDataset(
data_dir,
transform=transforms.Compose([
transforms.Grayscale(),
transforms.Resize(img_size),
transforms.ToTensor(),
]),
),
num_workers=0,
batch_size=1,
shuffle=True,
))
for i in range(10):
x0, x1, is_diff = next(data_iterator)
distance = (torch.pairwise_distance(*model(
to_tensor(x0, device=global_torch_device()),
to_tensor(x1, device=global_torch_device()),
)).cpu().item())
boxed_text_overlay_plot(
torchvision.utils.make_grid(torch.cat((x0, x1), 0)),
f"Truth: {'Different' if is_diff.cpu().item() else 'Alike'},"
f" Dissimilarity: {distance:.2f},"
f" Verdict: {'Different' if distance > threshold else 'Alike'}",
)
def vis(model, data_dir, img_size):
""" """
# ## Visualising some of the data
# The top row and the bottom row of any column is one pair. The 0s and 1s correspond to the column of the
# image.
# 0 indicates dissimilar, and 1 indicates similar.
example_batch = next(
iter(
DataLoader(
PairDataset(
data_path=data_dir,
transform=transforms.Compose([
transforms.Grayscale(),
transforms.Resize(img_size),
transforms.ToTensor(),
]),
split=SplitEnum.validation,
),
shuffle=True,
num_workers=0,
batch_size=8,
)))
concatenated = torch.cat((example_batch[0], example_batch[1]), 0)
boxed_text_overlay_plot(torchvision.utils.make_grid(concatenated),
str(example_batch[2].numpy()))
def stest_many_versus_many2(model: Module,
data_dir: Path,
img_size: Tuple[int, int],
threshold=0.5):
"""
:param model:
:type model:
:param data_dir:
:type data_dir:
:param img_size:
:type img_size:
:param threshold:
:type threshold:
"""
dataiter = iter(
DataLoader(
PairDataset(
data_dir,
transform=transforms.Compose([
transforms.Grayscale(),
transforms.Resize(img_size),
transforms.ToTensor(),
]),
),
num_workers=4,
batch_size=1,
shuffle=True,
))
for i in range(10):
x0, x1, is_diff = next(dataiter)
distance = (model(
to_tensor(x0, device=global_torch_device()),
to_tensor(x1, device=global_torch_device()),
).cpu().item())
boxed_text_overlay_plot(
torchvision.utils.make_grid(torch.cat((x0, x1), 0)),
f"Truth: {'Different' if is_diff.cpu().item() else 'Alike'},"
f" Dissimilarity: {distance:.2f},"
f" Verdict: {'Different' if distance > threshold else 'Alike'}",
)